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IMAGE PROCESSING

IMAGE PROCESSING. INTRODUCTION.

tashya-jimenez
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IMAGE PROCESSING

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  1. IMAGE PROCESSING

  2. INTRODUCTION • In electrical engineering and computer science image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. • Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.

  3. Definitions of Image Processing • •The general term "image processing" refers to a computer discipline wherein digital images are the main data object. This type of processing can be broken down into several sub-categories, including: compression, image enhancement, image filtering, image distortion, image display and coloring. • •Any activity that transforms an input image into an output image. Manipulation of an image to improve or change some quality of the image

  4. This Process involves two aspects • Improving the visual appearance of images to a human viewer. • Preparing images for measurement of the features and structures present.

  5. Why do we need it……? • Since the digital image is invisible it must prepare for viewing one or more output device. The digital image can be optimized for the application by enhancing or altering the appearance of the structures within it. • It might be possible to analyze the image in the computer and provide clues to the radiologist to help direct important/suspicious structure.

  6. Acquiring Images • Since the digital image is invisible it must prepare for viewing one or more output device. The digital image can be optimized for the application by enhancing or altering the appearance of the structures within it. • It might be possible to analyze the image in the computer and provide clues to the radiologist to help direct important/suspicious structure.

  7. Types of Image Transformation • Image-to-image transformation. • Image-to-information transformation. • Information-to-image transformation.

  8. Image to Image Transformation • Enhancement (make image more useful, pleasing). • Restoration (DE blurring, grid, line removal). • Geometry (scaling, sizing, zooming, morphing etc.).

  9. Image to Information • Image statistics (histograms). • Image compression. • Image Analysis (segmentation, feature extraction). • Computer aided detection and diagnosis (CAD).

  10. Information to Image Transformation • Depression of compressed image data. • Reconstruction of image. • Computer graphics, animation and virtual reality.

  11. High Resolution • The process of obtaining a high resolution (HR) image or a sequence of HR from a set of low resolution (LR) observation. • HR technique has applied to a variety of fields such as obtaining. • Improve still images. • High definition television. • High performance color liquid crystal display (LCD) screen. • Video surveillance. • Remote sensing and • Medical imaging.

  12. Color Spaces • Conversion from RGB (The brightness of individual red, green and green signal at defined wavelength) to YIQ/YUV and to other color encoding schemes is straightforward and losses no information.

  13. Image Sensors • Digital processing requires images to be obtained in the form of electrical signals. These signals can be digitized into sequence of numbers which can be processed by a computer.

  14. Image Intensity Equalization using Histograms • Image intensity Equalization is the process of converting the given image into the desired manner using Histogram. In histogram equalization we are trying to maximize the image contrast by applying a gray level transform which tries to flatten the resulting histogram. The gray level transform is a scaled version of the original image's cumulative histogram. That is, the gray level transform T is given by T[i] = (G-1)c(i), where G is the number of gray levels and c(i) is the normalized cumulative histogram of the original image. When we want to specify a non-flat resulting histogram, we can use the following steps: • Specify the desired histogram g(z) • Obtain the transform which would equalize the specified histogram, Tg, and its inverse Tg-1 • Get the transform which would histogram equalize the original image, s=T[i] • Apply the inverse transform Tg-1 on the equalized image, that is z=Tg-1[s]

  15. Input Image corresponding histogram Output Image corresponding histogram

  16. Multiple Images: It may constitute a series of views of the same area using different wavelength of light and other signals. Examples includes the image processed by satellites those images may require processing. • Hardware Requirement: • A general purpose computer can be used for image processing; four key demands must be met • high resolution image display • Sufficient memory transfer bandwidth. • Sufficient storage space • Sufficient computing power.

  17. Software Requirements: Adobe Photoshop, Corel draw, Serif photo plus Mat lab etc. CONCLUSION: Adobe The Image processing is used to get/acquire image in a desired manner by using some of the software’s shown above without affecting its original input image and their may not be loos of data during the conversion process.

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